Commodities such as gas, electric, and water are provided by utility companies around the world to households, businesses, and other consumers. Utility companies typically charge the consumers based on the quantity of the commodity that the consumer uses or consumes. Thus, utility companies commonly gauge consumption using meters and bill their customers accordingly. Traditionally, at the end of a reporting period, a utility employee would physically inspect and record each customer's meter readout dials, which reflect usage. More recently, many utility companies have deployed automatic meter reading systems that can automatically capture consumption data from the field. In many cases, adapter modules are fitted to existing meters to provide remote data collection capability. The modules typically collect the data and transmit it over wire and/or wireless communication methods so that the data is ultimately received by the utility company.
One of the primary functions of a utility data collection network is the ability to capture data for the purpose of establishing patterns of consumption. The data is used to determine not only how much the consumer is using, but when the user is consuming it. Collecting data for this purpose has been accomplished by frequently collecting the usage data and time stamping the usage data to associate it with a particular time. When done very often, this type of data collection is typically known as interval data collection. Interval data collection is generally based on set time intervals or sampling periods, i.e., data is recorded for set intervals of time. For example, many systems record the amount of energy consumed every 2.5 minutes. Transmission of the information also typically happens on a frequent basis, most often an integer number of times the sampling period, e.g., every 5 minutes (which is 2 times the 2.5 minute sampling period).
Recording and transmitting usage data at regular time intervals must be done frequently to provide enough information to reconstruct the consumer's usage pattern and often involves transmitting highly redundant information. For example, these systems will send data even if there is no consumption activity. This has serious consequences on battery-operated devices, creates RF traffic with potential for collisions and loss of data or reduction of performance, increases the amount of data to be transmitted, and requires significant amounts of processing power and memory space for data storage, post-processing and archiving.
Time-interval based utility data collection methods that use radio-frequency (RF) transmission also typically require various techniques to avoid data collision. Transmission time is generally randomized in each communication device to avoid collisions that may result if, for example, many modules resumed operation after a power outage and attempted to transmit at the same time. To randomize transmission time, separate randomizing modules are often implemented at each meter. Data corresponding to the energy consumed during a given time period is generally transmitted at some random point during a future time period. However, since it is usually desired that the data be associated with a specific and precise time period to show when the information was sampled from the meter, additional information has to be conveyed with the usage data identifying the difference between data capture time and data transmission time. Moreover, additional processing is usually required to calculate the time boundaries from the transmitted data.